3 citations · 5 across the 2 of their papers we have counts for
3 papers
Pseudo-Mallows for Efficient Probabilistic Preference Learning
Qinghua Liu, Valeria Vitelli, Carlo Mannino +2
We propose the Pseudo-Mallows distribution over the set of all permutations of items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has…
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model
Qinghua Liu, Andrew Henry Reiner, Arnoldo Frigessi +1
Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Curre…
BayesMallows: An R Package for the Bayesian Mallows Model
Øystein Sørensen, Marta Crispino, Qinghua Liu +1
BayesMallows is an R package for analyzing data in the form of rankings or preferences with the Mallows rank model, and its finite mixture extension, in a Bayesian probabilistic fr…